基于自编码器和LSTM的模型降阶方法

自编码器是一种有效的数据降维方法,可以学习到数据中的隐含特征,并重构出原始输入数据。本文提出了一种基于多层自编码器和长短期记忆网络的模型降阶方法,以提升降阶模型的精度。文中以二维圆柱绕流为例,对该方法进行了分析与验证。首先用多层自编码器对原始数据进行降阶和特征提取,然后构建基于长短期记忆网络的预测模型,最后将自编码器和预测模型拼接并进行微调,得到降阶模型,并将其与基于主成分分析的降阶模型进行对比。结果表明,多层自编码器能在保证精度的同时提升数据压缩率;提出的降阶方法有效地提升了模型精度,使得预测速度场和原速度场之间的均方根误差降低至3×10^-3左右。

Reduced order model based on autoencoder and long short-term memory network

Autoencoder is an effective dimensionality reduction method that can learn the hidden information and features implicated in the data,and reconstruct the original input data.We propose a model reduction method with improved accuracy based on a multi-layer autoencoder and a long short-term memory network.The method is analyzed and verified through a two-dimensional flow past a cylinder.Firstly,the multi-layer autoencoder is used to reduce the order and extract features of the original data.Then,a prediction model based on a long short-term memory network is established.At last,the autoencoder and the prediction model are spliced into a single network to obtain a fine-tuned reduced order model.This model is further compared with another one based on the principal component analysis.Results show that the multi-layer autoencoder can improve the data compression ratio while ensuring the accuracy.The proposed reduced order method can effectively improve the model accuracy since the root mean square error between the predicted and the original velocity fields is reduced to within 3×10-3.